“第二十一章 车牌识别”版本间的差异

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（以“ ==教学资料== *教学参考 *[http://aigraph.cslt.org/courses/21/course-21.pptx 课件] *小清爱提问：机器如何识别车牌[https://...”为内容创建页面）
 
（→‎演示链接）
 
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*[http://aigraph.cslt.org/courses/21/course-21.pptx 课件]
 
*[http://aigraph.cslt.org/courses/21/course-21.pptx 课件]
 
*小清爱提问：机器如何识别车牌[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247485046&idx=1&sn=4ca0402b4bc576d140c5d03d79879431&chksm=c3080cb4f47f85a2d5e73c1b4039f44f288a7c66d03d2be203dd1c33516dc7795bf286e2faaa&scene=178#rd]
 
*小清爱提问：机器如何识别车牌[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247485046&idx=1&sn=4ca0402b4bc576d140c5d03d79879431&chksm=c3080cb4f47f85a2d5e73c1b4039f44f288a7c66d03d2be203dd1c33516dc7795bf286e2faaa&scene=178#rd]
 +
*小清爱提问：目标检测中的YOLO网络长什么样？[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247489867&idx=1&sn=0daa55f14d6ccd8e201f3de4533a791f&chksm=c3081389f47f9a9fa4fcc90aaba0432cfb8aed471ef95574dc856c3619f699cc0f578c58e45a&scene=178&cur_album_id=3052762821081645063#rd]
  
  
 
==扩展阅读==
 
==扩展阅读==
  
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*  
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* AI100问：什么是YOLO模型 [http://aigraph.cslt.org/ai100/AI100-125-什么是YOLO网络.pdf]
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* AI100问：机器如何识别车牌[http://aigraph.cslt.org/ai100/AI-100-100-浅谈车牌识别.pdf]
  
 
==视频展示==
 
==视频展示==
  
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*  
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* YOLO visulaization [http://aigraph.cslt.org/courses/21/yolo2-plate.mp4]
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*  
+
* YOLO-v3 [http://aigraph.cslt.org/courses/21/YOLOv3-show.mp4]
 +
 
  
 
==演示链接==
 
==演示链接==
  
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*  
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* 旷世科技在线演示[https://www.faceplusplus.com.cn/license-plate-recognition/]
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*  
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* 云脉展示[https://www.yunmaiocr.com/goPlate#]
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*  
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* 薪火科技[https://www.xinhuokj.com/ocr.html]
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* DTK车牌识别在线演示[https://www.dtksoft.com/lprdemo]
  
 
==开发者资源==
 
==开发者资源==
  
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* Insight Face [https://github.com/deepinsight/insightface]
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* OpenCV 车牌识别流程和样例程序[https://codeantenna.com/a/XVQaHQ0m6T][https://github.com/longzix/SVM-/tree/master]
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* OpenCV [https://opencv.org/]
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* 基于Yolo v4的车牌识别 [https://learnopencv.com/automatic-license-plate-recognition-using-deep-learning/]
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* Face js: quick demo with JS [https://justadudewhohacks.github.io/face-api.js/docs/index.html]
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* ALPR in Unscontrained Scenarios [https://github.com/sergiomsilva/alpr-unconstrained]
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+
  
  
 
==高级读者==
 
==高级读者==
  
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* Brunelli R, Poggio T. Face recognition: Features versus templates[J]. IEEE transactions on pattern analysis and machine intelligence, 1993, 15(10): 1042-1052. [https://www.academia.edu/download/7233387/com-pami1993-10-01.pdf]
+
* Arafat M Y, Khairuddin A S M, Khairuddin U, et al. Systematic review on vehicular licence plate recognition framework in intelligent transport systems[J]. IET Intelligent Transport Systems, 2019, 13(5): 745-755. [https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2018.5151]
−
* Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 1891-1898. [https://openaccess.thecvf.com/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf]
+
* Srikanth P, Kumar A. Automatic vehicle number plate detection and recognition systems: Survey and implementation[M]//Autonomous and Connected Heavy Vehicle Technology. Academic Press, 2022: 125-139. [https://www.sciencedirect.com/science/article/pii/B9780323905923000070]
−
* Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. DeepFace: Closing the gap to human-level performance in face verification. In Proc. CVPR, 2014.[https://openaccess.thecvf.com/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf]
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* Zherzdev S, Gruzdev A. Lprnet: License plate recognition via deep neural networks[J]. arXiv preprint arXiv:1806.10447, 2018. [https://arxiv.org/pdf/1806.10447]
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* 王东，利节，许莎， 人工智能，第一章，认识你的脸，2019 [http://aibook.cslt.org]
+
* Xie L, Ahmad T, Jin L, et al. A new CNN-based method for multi-directional car license plate detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(2): 507-517. [https://trid.trb.org/view/1500448]
 +
* J. Redmon, S. Divvala, R. Girshick, A. Farhadi You only look once: unified, real-time object detection Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2016), pp. 779-788 [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf]

2023年8月13日 (日) 01:54的最后版本


教学资料

  • 教学参考
  • 课件
  • 小清爱提问：机器如何识别车牌[1]
  • 小清爱提问：目标检测中的YOLO网络长什么样？[2]


扩展阅读

  • AI100问：什么是YOLO模型 [3]
  • AI100问：机器如何识别车牌[4]

视频展示

  • YOLO visulaization [5]
  • YOLO-v3 [6]


演示链接

  • 旷世科技在线演示[7]
  • 云脉展示[8]
  • 薪火科技[9]
  • DTK车牌识别在线演示[10]

开发者资源

  • OpenCV 车牌识别流程和样例程序[11][12]
  • 基于Yolo v4的车牌识别 [13]
  • ALPR in Unscontrained Scenarios [14]


高级读者

  • Arafat M Y, Khairuddin A S M, Khairuddin U, et al. Systematic review on vehicular licence plate recognition framework in intelligent transport systems[J]. IET Intelligent Transport Systems, 2019, 13(5): 745-755. [15]
  • Srikanth P, Kumar A. Automatic vehicle number plate detection and recognition systems: Survey and implementation[M]//Autonomous and Connected Heavy Vehicle Technology. Academic Press, 2022: 125-139. [16]
  • Zherzdev S, Gruzdev A. Lprnet: License plate recognition via deep neural networks[J]. arXiv preprint arXiv:1806.10447, 2018. [17]
  • Xie L, Ahmad T, Jin L, et al. A new CNN-based method for multi-directional car license plate detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(2): 507-517. [18]
  • J. Redmon, S. Divvala, R. Girshick, A. Farhadi You only look once: unified, real-time object detection Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2016), pp. 779-788 [19]
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